4 citations · 11 across the 22 of their papers we have counts for
11 papers · 1 filter
Most-Recent Anchoring with Recurrent Ordering for Time Series Forecasting
Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak +2
Long-term forecasting models commonly process all patches in a look-back window using the same fixed stack. Older contextual patches and recent evidence therefore receive the same…
Dual-Context Analog Retrieval for Time Series Forecasting
Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak +2
Most long-term time-series forecasting models map the look-back window directly to the full horizon in a single pass. While efficient, this design does not explicitly identify whic…
MixBench-TS: A Multivariate Time Series Forecasting Benchmark Where Channel Mixing Pays Off
Ibram Abdelmalak, Mischa Putzke, Jungmin Choi +3
Multivariate Time Series Forecasting (MTSF) models that mix information across channels assume that the past of one channel carries information about the future of another. Yet the…
Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting
Kiran Madhusudhanan, Christian Klötergens, Lars Schmidt-Thieme +1
Probabilistic forecasting plays an essential role in risk-sensitive decision-making, particularly in long-horizon settings. However, existing approaches often face a fundamental tr…
Do Tabular Foundation Models Agree with Themselves?
Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme +1
Tabular Foundation Models (TFMs) are the state of the art for many tabular prediction tasks. They are transformers that approximate the Bayesian posterior predictive distribution u…
CAMP: A Cycle-Aware Multi-Scale Patch Mixer for Time Series Forecasting
Jung Min Choi, Vijaya Krishna yalavarthi, Lars Schmidt-Thieme
Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individual input windows. Existing c…
The Importance of Encoder Choice:A Tabular-Image Study
Ilia Koloiarov, Diego Coello de Portugal Mecke, Vijaya Krishna Yalavarthi +2
Multimodal learning usually requires a dedicated encoder per modality. When a tabular modality is involved, prior work has been mostly using a \emph{plain MLP} as the encoder. Yet…
Gaussian Mixture Copula Processes for Irregular Time Series
Christian Klötergens, Tom Hanika, Lars Schmidt-Thieme +1
We introduce Gaussian Mixture Copula Processes (GMCP), a conditional copula process for irregularly sampled multivariate time series (IMTS) that is expressive and marginalization c…
Probabilistic Circuits for Irregular Multivariate Time Series Forecasting
Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
Joint probabilistic modeling is essential for forecasting irregular multivariate time series (IMTS) to accurately quantify uncertainty. Existing approaches often struggle to balanc…
NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
Multivariate time series forecasting remains a challenge due to the complexity of local temporal dynamics and global dependencies across multiple variables. In this paper, we propo…
HPMixer: Hierarchical Patching for Multivariate Time Series Forecasting
Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
In long-term multivariate time series forecasting, effectively capturing both periodic patterns and residual dynamics is essential. To address this within standard deep learning be…